Evidence map›Paper›PMID 32393838›Full record

ReviewNature human behaviour2020

Neutral syndrome.

Armand M Leroi, Ben Lambert, James Rosindell, Xiangyu Zhang, Giorgos D Kokkoris

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature human behaviour, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Armand M LeroiDepartment of Life Sciences, Imperial College London, London, UK. a.leroi@imperial.ac.uk.ORCID http://orcid.org/0000-0002-5603-0351
Ben LambertMRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.ORCID http://orcid.org/0000-0003-4274-4158
James RosindellDepartment of Life Sciences, Imperial College London, Ascot, UK.ORCID http://orcid.org/0000-0002-5060-9346
Xiangyu ZhangData Science Institute, Imperial College London, London, UK.
Giorgos D KokkorisDepartment of Marine Sciences, University of the Aegean, Mytilene, Greece.ORCID http://orcid.org/0000-0002-9342-6566

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neutral models of evolution assume the absence of natural selection. Formerly confined to ecology and evolutionary biology, neutral models are spreading. In recent years they've been applied to explaining the diversity of baby names, scientific citations, cryptocurrencies, pot decorations, literary lexica, tumour variants and much more besides. Here, we survey important neutral models and highlight their similarities. We investigate the most widely used tests of neutrality, show that they are weak and suggest more powerful methods. We conclude by discussing the role of neutral models in the explanation of diversity. We suggest that the ability of neutral models to fit low-information distributions should not be taken as evidence for the absence of selection. Nevertheless, many studies, in increasingly diverse fields, make just such claims. We call this tendency 'neutral syndrome'.

Indexed as

Biological EvolutionModels, BiologicalSelection, GeneticAnimalsBiodiversityGenetic DriftMutation

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.